Sameer Singh

About

Sameer Singh is a Professor of Computer Science at UC Irvine. His primary research focuses on the robustness and interpretability of machine learning algorithms and models that reason with text and structure for natural language processing. He has worked as a postdoctoral researcher at the University of Washington and earned his Ph.D. from the University of Massachusetts, Amherst. Dr. Singh has been recognized with several awards, including being named the Kavli Fellow by the National Academy of Sciences, receiving the NSF CAREER award, the UCI Distinguished Early Career Faculty award, the Hellman Faculty Fellowship, and being selected as a DARPA Riser. His research group has received funding from notable organizations such as the Allen Institute for AI, Amazon, NSF, DARPA, Adobe Research, Hasso Plattner Institute, NEC, Base 11, and FICO. He has published extensively in machine learning and natural language processing venues and has received conference paper awards at KDD 2016, ACL 2018, EMNLP 2019, AKBC 2020, ACL 2020, and NAACL 2022.

Research topics

• Computer Science
• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• Programming language
• Data Mining
• Geology
• Computer network
• Distributed computing
• Human–computer interaction

Selected publications

• AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

  2020 · 1160 citations

  Senior authorCorresponding

  The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. Reformulating tasks as fillin-the-blanks problems (e.g., cloze tests) is a natural approach for gauging such knowledge, however, its usage is limited by the manual effort and guesswork required to write suitable prompts. To address this, we develop AUTOPROMPT, an automated method to create prompts for a diverse set of tasks, based on a gradient-guided sea…

  DOI

• Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing Systems

  IEEE Access · 2020 · 77 citations

  Senior authorCorresponding

  As the complexity of Deep Neural Network (DNN) models increases, their deployment on mobile devices becomes increasingly challenging, especially in complex vision tasks such as image classification. Many of recent contributions aim either to produce compact models matching the limited computing capabilities of mobile devices or to offload the execution of such burdensome models to a compute-capable device at the network edge - the edge servers. In this paper, we propose to modify the structure a…

  DOI

• Benchmark Data Repositories for Better Benchmarking

  arXiv (Cornell University) · 2024-10-31 · 5 citations

  preprintOpen access

  In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets. While a growing body of work establishes guidelines for -- and levies criticisms at -- data and benchmarking practices in machine learning, comparatively less attention has been paid to the data repositories where these datasets are stored, documented, and shared. In this paper, we analyze the landscape of these $\textit{benchmark data repositories}$ and the role they can play…

  PublisherOA PDFDOI

• Modular Framework for Visuomotor Language Grounding

  arXiv (Cornell University) · 2021 · 5 citations

  Senior authorCorresponding

  Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research. However, data collection for these tasks is expensive and end-to-end approaches suffer from data inefficiency. We propose the structuring of language, acting, and visual tasks into separate modules that can be trained independently. Using a Language, Action, and Vision (LAV) framework removes the dependence of action and vision modules on instruction following datasets, making t…

  PublisherOA PDFDOI

• Role of Physical and Biochemical Characters in Groundnut Genotypes as a Basis of Resistance against Groundnut Bruchid, Caryedon serratus Olivier during Storage

  Legume Research - An International Journal · 2024-07-09 · 2 citations

  articleOpen accessSenior author

  Background: Groundnut bruchid (Caryedon serratus Olivier) is the most important stored grain insect pests of groundnut that significantly lowers the quality and market acceptance of the produce. The grub of this insect causes extensive damage to the kernels by boring into undamaged shell and feeds on seeds internally. Therefore, the present study is aimed at screening of the groundnut genotypes on the basis of their physical and biochemical characters which are responsible for imparting resistan…

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: RI: Small: Post hoc Explanations in the Wild: Exposing Vulnerabilities and Ensuring Robustness

  NSF · $225k · 2020–2023

• RI: Small: Modeling Multiple Modalities for Knowledge-Base Construction

  NSF · $448k · 2018–2022

• CRII: RI: Explaining Decisions of Black-box Models via Input Perturbations

  NSF · $175k · 2018–2021

Frequent coauthors

• Matt Gardner

  Duke Institute for Health Innovation

  110 shared

• Robert L. Logan

  61 shared

• Eric Wallace

  49 shared

• Dheeru Dua

  30 shared

• Nitish Gupta

  National Institute of Technology Warangal

  30 shared

• Sebastian Riedel

  29 shared

• Pouya Pezeshkpour

  27 shared

• Dylan Slack

  27 shared

Education

• PhD, Computer Science

  University of Massachusetts Amherst

  2014

• MS, EECS

  Vanderbilt University

  2007

Awards & honors

• Kavli Fellow by the National Academy of Sciences
• NSF CAREER award
• UCI Distinguished Early Career Faculty award
• Hellman Faculty Fellowship
• Selected as a DARPA Riser

Similar researchers at University of California, Irvine

• Pierre Baldih-121
• Gene Tsudikh-82
• Padhraic Smythh-78